Developing a Platform Using Petri Nets and GPenSIM for Simulation of Multiprocessor Scheduling Algorithms

A multitasking operating system allows many programs to run at the same time. We need to schedule our Central Processing Units (CPUs) efficiently to handle these services. CPU scheduling algorithms come in a score of forms and any strategy has advantages and disadvantages. We implemented a Multi-Level Feedback Queue (MLFQ) algorithm with static time quantum for the first queue, making an efficient scheduling algorithm of MLFQ dynamic time quantum used in the second queue. Other than that, we compared both results and observed which dynamic time quantum delivers the best result. In the first queue, we used the Round Robin (RR) algorithm with less quantum time for processes that have a short burst time. The process that had more burst time than the defined quantum in the first queue was allowed to move into the second queue. Here, we calculated the average burst time of all remaining processes and defined that burst time as a quantum for the second queue after the Shortest Job First (SJF) scheme was applied in this queue. As a result, 70% of the processes in the second queue was received CPU. Other processes moved into the lower queue (the last queue). Hence, this technique prevented the starvation problem for those processes which have large burst times.

Computers are taking up great part in technology evolution. They perform many tasks quickly and reliably when compared to humans, opening new avenues. One important aspect of computers is multitasking that enables a system to execute multiple tasks at a time. Scheduler is an object of the kernel that divides CPU time among various processes, based on any specific policy including, but not limited to, response time, throughput, and interactivity. Scheduler works differently for single core and multi-core processor environment. There are different scheduling algorithms for multicore processor based on required performance criteria. In this work we have proposed and observed the simulation of different scheduling algorithms at user level for single core and multicore and compared the different metrics like average waiting time, turnaround time, CPU utilization and number of context switches in graphical manner and observed that the performance improvement in multicore environment compared to single core processor.

This paper presents a dynamic central processing unit (CPU) scheduling algorithm selectionsystem that utilizes machine learning to optimize the process execution within a computer system. Thisstudy evaluated six scheduling algorithms based on key performance indicators such as CPUutilization, turnaround time, waiting time, response time, and throughput. To overcome the limitationsof traditional approaches, the proposed methodology integrates a multilayer neural network (MLNN)to identify patterns and dynamically choose the most appropriate algorithm at runtime. The paperreviews related literature, highlighting efforts to enhance scheduling algorithms through machinelearning and improvements to conventional methods. The methodology includes developing a processdatabase, applying scheduling algorithms, optimizing the outcomes, and training a neural network fordynamic algorithm selection. The results indicate that the proposed approach outperforms existingalgorithms regarding waiting time, turnaround time, throughput, and execution efficiency.Additionally, the methodology offers adaptability to diverse process parameters. The study concludesby underscoring the potential for future advancements, including improvements in algorithmprecision, incorporation of additional process characteristics, exploration of advanced patternrecognition techniques, and integration of security measures for real-world applications such as cloudcomputing, edge computing, and IoT (Internet of things) environments.This paper presents a dynamiccentral processing unit (CPU) scheduling algorithm selection system that utilizes machine learning tooptimize the process execution within a computer system. This study evaluated six schedulingalgorithms based on key performance indicators such as CPU utilization, turnaround time, waitingtime, response time, and throughput. To overcome the limitations of traditional approaches, theproposed methodology integrates a multilayer neural network (MLNN) to identify patterns anddynamically choose the most appropriate algorithm at runtime. The paper reviews related literature,highlighting efforts to enhance scheduling algorithms through machine learning and improvementsto conventional methods. The methodology includes developing a process database, applyingscheduling algorithms, optimizing the outcomes, and training a neural network for dynamic algorithmselection. The results indicate that the proposed approach outperforms existing algorithms regardingwaiting time, turnaround time, throughput, and execution efficiency. Additionally, the methodologyoffers adaptability to diverse process parameters. The study concludes by underscoring the potentialfor future advancements, including improvements in algorithm precision, incorporation of additionalprocess characteristics, exploration of advanced pattern recognition techniques, and integration ofsecurity measures for real-world applications such as cloud computing, edge computing, and IoT(Internet of things) environments.

These days, the problems of scheduling and security are increasingly pressing as a result of the rapidly growing number of implementations that are operated in operating systems. Process scheduling and scheduling security is the most effective methodology of operating a system which can improve and degrade the performance of the computer system. The performance of scheduling algorithm in operating system can be measured by scheduling principles like average waiting time, average turnaround time, response time, throughput etc. Much research carried out over operating system security and round robin scheduling used in time sharing operating system in previous years to resolve the issues regarding to improvement in the performance of round robin (RR) scheduling and improving the security in the scheduling approach. This study discussed the security issues in operating systems, scheduling security and discrete structure of a new approach for RR scheduling. The researcher has suggested a list of policy for scheduling method security and proposed a new approach for RR scheduling and investigated the scheduling principles. The proposed methodology covers distinctive strategy for selection manner of process from ready queue than RR and used diverges time quantum. The proposed methodology has been validated experimentally by comparing with a numerous scheduling algorithm namely as Round Robin (RR), Adaptive Round Robin (ARR), Round Robin Remaining time (RRRT), improved Round Robin (IRR), an additional improvement on the improved Round Robin (AAAIRR), and an Enhanced Round Robin (ERR). Numerous simulations have been conducted to validate the effectiveness of the proposed algorithm to compelling the performance of system. At the conclusion the performance of computing system is dependent on security of operating system as well as secluding methods.

The performance of the multiprocessor system and time-sharing system rely upon CPU scheduling algorithm. Some of the well known efficient CPU scheduling algorithms are Round Robin scheduling (RR), Shortest job first(SJF), Preemptive version of SJF(SRTF), First Come First Serve (FCFS) etc. As of now, the Round Robin scheduling algorithm is considered as the efficient process scheduling algorithm among all the existing CPU scheduling algorithms. However, in RR the shortest one have to wait for a longer time and in SRTF longer process behaves as a suspended process as short tasks keep on executing. This paper proposes a new scheduling process algorithm ESRR (Efficient Shortest Remaining Time Round Robin) that consolidate two of the preemptive version of existing scheduling algorithms namely RR and SRTF. ESRR reduces total waiting time and turn around time in compare to RR and reduces the waiting time of the shorter process and it also provides a longer process to execute faster than SRTF.

Delivery of goods will normally be conducted in accordance with the queuing time of booking. Sometimes, it is inefficient and results in a delay on the delivery of goods. Therefore, to make a better scheduling system, the Shortest Job First and Round Robin algorithms been implemented. From the results of experiments, Shortest Job First and Round Robin algorithms successfully applied to the scheduling delivery application. Shortest Job First algorithm is better than Round Robin scheduling in the case of delivery of goods, because the algorithm execution process which takes small time will be moved before the process which takes much time, so it needs smaller time than using Round Robin algorithm. Index Terms - Round Robin, Scheduling, Shipping, Shortest Job First

An operating system is software that is designed to manage computer hardware and software resources. However, this management requires applying an ample number of techniques and algorithms which are called synchronization and scheduling. The scheduling algorithms are used to arrange the way that the CPU is assigned to the processes, while synchronization is utilized to indicate how to work with multi-processes at the same time. Therefore, they are related to each other. CPU scheduling is a vital phenomenon of an operating system. At present, numerous CPU scheduling algorithms exist as First Come First Serve) FCFS(, Shortest Job First (SJF), Shortest Remaining Time First (SRTF), Priority Scheduling, and Round Robin (RR). In this paper, a survey of the current synchronization and scheduling algorithms have been presented. An overview of each technique with the main algorithms have been described in detail with the advantages and the issues of each algorithm. Furthermore, this paper has dug deep into the real-time operating system scheduling issues, which is the current trend in operating system researches.

Scheduling of a set of tasks in multiprocessor environment is a computationally intensive job. There are primarily two broad approaches for scheduling of tasks on multiprocessors. In one of the approaches tasks are allocated to processor in the beginning (partition based strategy) and the other approaches maintain a global scheduler. There exist a large volume of work in multiprocessor scheduling having different optimization objectives such as schedule length, response time, processor utilization, etc. However, most of these works focus only in preemptive scheduling. Very less attention has been given for scheduling of non-preemptive tasks. In this work, we present a methodology for scheduling of a set of non-preemptive real-time tasks using minimum number of processors. We consider that the tasks are allocated to the processors using bin-packing strategies such as firstfit or best-fit and non-preemptive Earliest Deadline First (npEDF) scheduling method is applied to each processor. We did extensive experiments by combining different partitioning strategies with the ordering of tasks (period, utilization, etc). We found that First-Fit Increasing Period, Best-Fit Increasing Period, First-Fit Decreasing Utilization and Best-Fit Decreasing Utilization give reasonably good results. The success ratio of decreasing utilization heuristics are 10%–30% more than increasing period heuristics. We observed that First-Fit Decreasing Utilization and BestFit Decreasing Utilization takes more time than First-Fit Increasing Period and Best-Fit Increasing Period. We also compared our results with the existing approach.

The paper describes investigations into multiprocessor scheduling techniques for high quality audio DSP. A DSP processor/multiprocessor simulator is introduced which allows register-level simulation of multiprocessor DSP architectures and parallel DSP algorithms. The simulator has been used to investigate the implementation of recursive filtering algorithms for audio, using multiprocessor DSP architectures. Results from these studies are presented and discussed. Also described are multiprocessor DSP algorithm mapping and scheduling techniques based on genetic algorithms. These investigate techniques using genetic algorithms to schedule audio DSP algorithms written in the form of data flow graphs onto specified DSP multiprocessor arrays. The basic techniques are described and initial results from these studies are also presented and discussed. (8 pages)

The Shortest Job First (SJF) algorithm reduces the average waiting time of processes in a CPU environment; however, it may lead to the starvation of long processes. The Round Robin (RR) algorithm solves starvation by equally distributing processing time. This paper explores user-friendly improvements by allowing users to specify which processes should be finished first. The SJF, RR, and the user-priority SJFRR were compared using four test cases in the following criteria: context switching, average waiting time, and average turnaround time. The user-priority SJFRR was found to have reduced average waiting time and average turnaround time in all test cases except for the first. Further research is also recommended in order to find out whether the algorithm's performance is attributed to the randomized assignment of priority and to find a proper basis for user designation of priority tasks.

CPU scheduling is the basis of multi-programmed operating systems. The scheduler is accountable for multiplexing processes on the CPU. By switching the CPU among processes, the operating system results in the computer more prolific. Various CPU scheduling algorithms exist for a multi-programmed operating system like First Come First Served (FCFS), Shortest Job First (SJF), Shortest Remaining Time Next (SRTN) and Round Robin (RR) scheduling algorithm. This paper has threefold objective. Firstly, it throws light on a new-fangled CPU scheduling algorithm called MIN-MAX whichbehaves as both preemptive and non-preemptive algorithm basis on the burst time. Secondly, it would helps to understand how MIN-MAXalgorithm improves the CPU efficiency in multi programming operating system and also trims down the starvation problem amongprocesses. Thirdly, it focuses on the comparative study of the existing algorithms on basis of various scheduling parameters with the proposed algorithm MIN-MAX.

This paper proposed an efficient real time scheduling algorithm using global scheduling paradigm running in multicore environment known as Global Preemptive Utility Accrual Scheduling (GPUAS) algorithm. The existing TUF/UA multiprocessor scheduling algorithms known as Greedy-Global Utility Accrual (G-GUA) and Non Greedy-Global Utility Accrual (NG-GUA) algorithms is seen to overlook the efficiency on its task scheduling algorithm. These algorithms have adapted the task migration attribute considering the load balancing problem in multi core platform. The existing PUAS uniprocessor scheduling algorithm is mapped into the multicore scheduling environment that consists of the global scheduling schemes considering the migration attribute of the executed tasks. The main principal of global scheduling is that it allows the executed tasks to migrate from one processor to the other processors whenever a scheduling event occurs in the system. The proposed GPUAS algorithm inherits the characteristics of PUAS in uniprocessor where it can preempt the highest PUD task at any event that occurs in the system. In this research, the proposed GPUAS algorithm enhanced the existing NG-GUA and G-GUA algorithms. The developed simulator has derived the set of parameter, events and performance metrics according to a detailed analysis of the base model. The proposed GPUAS algorithm achieved the highest accrued utility for the entire load range. The proposed GPUAS algorithm is more efficient than the existing algorithms, producing the highest accrued utility ratio and less abortion ratio making it more suitable and efficient for real time application domain.

Load balancing in cloud computing assigning the all load over each node of a mutual or shared system to make resource usage effective and to perform the response time to the job. This has used Round Robin (RR) and shortest job first (SJF) algorithm. Scheduling is the main part of cloud computing and jobs or workflow scheduling under the platform as a service. The work under infrastructure as a service work to virtual machine scheduling which machine is decided by scheduler should go on which job or virtual machine. Simply RR is a time quantum-based preemptive algorithm, and SJF is a burst time-based non-preemptive and preemptive algorithm. But RR work in two-phase like expected finish time adapted for each process. Each process with generally minimum completion times scheduled. This algorithm result as no longer waiting for the smallest task.

Partitioning and global scheduling are two approaches for scheduling real-time tasks in multiprocessor environments. Partitioning is the more favored approach, although it is sub-optimal. This is mainly due to the fact that popular uniprocessor real-time scheduling algorithms, such as EDF and RM, can be applied to the partitioning approach with low scheduling overhead. In recent years, much research has been done on global real-time multiprocessor scheduling algorithms based on the concept of “proportionate fairness”. Proportionate fair (Pfair) scheduling [5],[6] is the only known optimal algorithm for scheduling real-time tasks on multiprocessor. However, frequent preemptions caused by the small quantum length for providing optimal scheduling in the Pfair scheduling make it impractical. Deadline Fair Scheduling (DFS) [1] based on Pfair scheduling tried to reduce preemption-related overhead by means of extending quantum length and sharing a quantum among tasks. But extending quantum length causes a mis-estimation problem for eligibility of tasks and a non-work-conserving problem. In this paper, we propose the Enhanced Deadline Fair Scheduling (E-DFS) algorithm to reduce preemption-related overhead. We show that E-DFS allows us to decrease quantum length by reducing overhead and save wasted CPU time that is caused by preemption-related overhead and miss-estimation of eligibility.

Cluster computing has emerged as a key parallel processing platform for large scale data. All major internet companies use it as their major central processing platform. One of cluster computing's most popular examples is MapReduce and its open source implementation Hadoop. These systems were originally designed for batch and massive-scale computations. Interestingly, over time their production workloads have evolved into a mix of a small fraction of large and long-running jobs and a much bigger fraction of short jobs. This came about because these systems end up being used as data warehouses, which store most of the data sets and attract ad hoc, short, data-mining queries. Moreover, the availability of higher level query languages that operate on top of these cluster systems proliferated these ad hoc queries. Since existing systems were not designed for short, latency-sensistive jobs, short interactive jobs suffer from poor response times. In this paper, we present Piranha--a system for optimizing short jobs on Hadoop without affecting the larger jobs. It runs on existing unmodified Hadoop clusters facilitating its adoption. Piranha exploits characteristics of short jobs learned from production workloads at Yahoo! clusters to reduce the latency of such jobs. To demonstrate Piranha's effectiveness, we evaluated its performance using three realistic short queries. Piranha was able to reduce the queries' response times by up to 71%.